What if letting an agent evolve its own workflow just meant it got better at gaming the test? This paper tackles exactly that overfitting problem. Title: RRSI: Regularized Recursive Self-Improvement of Agent Harnesses URL: ❓ What's a harness? It's everything wrapped around the frozen LLM: prompts, control flow, tooling, memory, context management. The same model can perform very differently depending on this design. ❓ Why does self-improving it overfit? Three failure modes show up: fitting too tightly to the evolve-set benchmark, chasing noise, and letting complexity pile up unchecked. 💡 How does RRSI fix it? It applies classic ML regularization (L0, L1, L2) to harness evolution: an annealed edit budget limits changes per round, benchmark-specific proposals get screened out at selection, and unproductive components get pruned. 💡 What's the payoff? Across 8 benchmarks, RRSI beats baselines by up to 22.9% on unseen tasks, while using 30% fewer tokens. It feels like a genuinely grounded step toward agents that can safely improve their own workflow in production. #AIAgents# #SelfImprovement#